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  It is a 100% Onsite position in New York, NY

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  We are seeking a Lead Snowflake Data Engineer to design, own, and deliver end-to-end data engineering solutions in modern cloud environments.
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  This role focuses on building scalable, high-performance data pipelines using Snowflake and Cortex AI, with full lifecycle ownership—from ingestion and transformation to modelling, optimization, and consumption.

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  Lead the design and development of end-to-end ELT pipelines using Snowflake
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  Architect scalable data models optimized for performance, cost, and analytics consumption
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  Build and maintain backend data services using Python and PySpark
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  Leverage Snowflake Cortex AI to enable advanced analytics and intelligent data products
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  Drive performance tuning across pipelines, including query optimization, clustering, and warehouse scaling
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  Enforce best practices in data governance, security, and compliance
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  Collaborate across business, analytics, and engineering teams to deliver high-quality solutions
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  Provide technical leadership and mentorship to engineering teams
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  Communicate architecture decisions and trade-offs effectively in client-facing environments

Requirements

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  10+ years of experience, or equivalent ownership of production-grade data platforms
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  Deep expertise in:

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  Snowflake (data modeling, performance tuning, optimization)
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  Python and PySpark
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  Advanced SQL

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  Proven ability to design and deliver end-to-end data pipelines (ingestion transformation modeling consumption) in cloud environments (AWS preferred)
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  Required
  : Ownership of at least one production-grade Snowflake pipeline end-to-end
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  Strong foundation in modern data warehousing:

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  Dimensional modeling (star/snowflake schemas)
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  ELT/ETL design patterns
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  Data marts and optimization strategies

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  Experience with distributed data processing and large-scale datasets
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  Hands on experience with Snowflake Cortex AI integration
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  Working knowledge of React.js or similar frameworks
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  Strong understanding of data governance, security, and compliance
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  Ability to:

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  Clearly explain and defend architectural decisions
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  Design systems that perform reliably at scale
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  Balance performance, cost, and maintainability

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  Candidates should be able to clearly explain and apply the following in real-world scenarios:
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  Snowflake Performance & Scaling

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  Warehouse scaling modes (auto-scale, multi-cluster) and when to use them
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  Clustering keys and performance trade-offs
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  Cost vs performance optimization strategies

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  Snowflake Storage & Optimization

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  Micro-partitioning and its impact on pruning and query performance
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  Practical optimization techniques for large datasets

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  End-to-End Pipeline Design

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  Designing a complete ELT pipeline using Snowflake
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  Deciding where transformations should occur (Snowflake vs external processing)
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  Ensuring scalability, maintainability, and performance across the pipeline Engagement.